廖曼, 马腾, 郑倩琳, 刘妍君, 罗飞. 淮河流域农业生态系统中地下水体氮源追溯[J]. 中国生态农业学报(中英文), 2019, 27(5): 665-676. DOI: 10.13930/j.cnki.cjea.180763
引用本文: 廖曼, 马腾, 郑倩琳, 刘妍君, 罗飞. 淮河流域农业生态系统中地下水体氮源追溯[J]. 中国生态农业学报(中英文), 2019, 27(5): 665-676. DOI: 10.13930/j.cnki.cjea.180763
LIAO Man, MA Teng, ZHENG Qianlin, LIU Yanjun, LUO Fei. Tracing groundwater nitrogen source in Huai River Basin agro-ecosystem[J]. Chinese Journal of Eco-Agriculture, 2019, 27(5): 665-676. DOI: 10.13930/j.cnki.cjea.180763
Citation: LIAO Man, MA Teng, ZHENG Qianlin, LIU Yanjun, LUO Fei. Tracing groundwater nitrogen source in Huai River Basin agro-ecosystem[J]. Chinese Journal of Eco-Agriculture, 2019, 27(5): 665-676. DOI: 10.13930/j.cnki.cjea.180763

淮河流域农业生态系统中地下水体氮源追溯

Tracing groundwater nitrogen source in Huai River Basin agro-ecosystem

  • 摘要: 淮河流域地下水体中的氮污染问题一直以来备受关注。为了从源头追溯氮污染物的来源,本文通过清单法收集淮河流域2002—2017年期间35个地级市的农业统计资料,首先构建基于化肥施用氮、人畜粪便返田氮、生物固氮、大气沉降氮、种子带入氮、秸秆带入氮为输入项和作物收获氮、反硝化脱氮、氨挥发脱氮为输出项的氮平衡模型,估算进入淮河流域农业生态系统内的氮盈余量和强度;然后利用氮盈余量与淋滤系数构建氮淋滤模型定量估算氮淋滤到地下水体中的量。研究发现:2002—2017年间淮河流域农业生态系统中氮年均输入量为1 005.01万t·a-1,化肥施用氮是最大的氮输入源,占总输入量的52.76%;淮河流域农业生态系统中氮年均输出量为706.43万t·a-1,作物收获氮在氮输出中所占的比例最大,达87.29%。随着时间的增加,氮盈余量和强度逐步降低。本次从地级市角度计算的氮源强度和其时间变化规律与以往从流域角度计算的氮源强度和其时间变化规律相差不大,保证了结果的准确性。从地区上分析,河南省各地级市的氮源强度最高,山东省和安徽省各地级市的最低。2002—2017年间,淮河流域农业区氮盈余量淋滤进入地下水中的氮污染物总量为26.22万~41.71万t·a-1,淋滤进入到地下水体中的氮污染物平均量为31.41万t·a-1,其中2006年最高。较大的氮淋滤值对水体环境造成了较大的污染负荷。采用SPSS 21.0中用F统计量和皮尔森相关系数(ρ)对地下水中的实际氮污染物浓度与估算值间的氮污染物量进行相关性检验,最终通过显著性检验且相关系数达到0.517,证实了本次模型选择的准确性。本文研究表示,2002—2017年淮河流域农业生态系统中地下水体中氮的来源主要为化肥输入,最主要的输出途径为作物收获,污染最严重年份为2006年,为解决农业面源污染问题提供了重要的前期资料,对地下水中氮污染的防控具有重要的现实意义。

     

    Abstract: Nitrogen pollution in groundwater systems in Huai River Basin has drawn a lot of attention. In order to trace the source of nitrogen pollution in groundwater, 2002-2017 agricultural statistics data for 35 cities in the Huai River Basin agro-ecosystem were collected. A nitrogen balance model was set up based on nitrogen input and output in Huai River Basin, and it was used to calculate nitrogen surplus and intensity in the basin. Nitrogen input included input from fertilizers, humans & animal excreta, atmospheric deposition, biological fixation, seed nitrogen and straw nitrogen. Nitrogen output included crop harvest, denitrification and ammonia volatilization output. Also, combined nitrogen surplus and leaching coefficient, the nitrogen leaching model was built to quantitatively estimate the amount of nitrogen leaching into groundwater bodies from agro-ecosystem in Huai River Basin. The results showed that average nitrogen input in Huai River Basin agro-ecosystem was up to 10 050 100 t·a-1 for the 2002-2017, fertilizer input was the largest source of this amount nitrogen input and it accounted for 52.76%. Average nitrogen output was up to 7 064 300 t·a-1 for the period 2002-2017, crop harvest was the largest amount output of this amount nitrogen and it accounted for 87.29%. Nitrogen surplus and nitrogen source intensity decreased gradually with time for the period from 2002 to 2017. Nitrogen source intensity result was the same with previous studies, which ensured the accuracy of the results. At the regional aspects, the city in Henan Province had the highest nitrogen source intensity, while the cities in Shandong and Anhui Provinces had the lowest nitrogen source intensity. The amount of nitrogen that leached into the groundwater in Huai River Basin agro-ecosystem was 2.622×105-4.171×105 t·a-1, with the highest amount in 2006. The average nitrogen amount in groundwater was 3.141×105 t·a-1 for the period from 2002 to 2017, which caused a large pollution load in the water environment. F statistic and ρ value tests in SPSS 21.0 gave the relationship between the actual nitrate concentration in groundwater and the estimation nitrogen amount leaching into the groundwater. Finally, the estimated and observed values passed significance test, with a correlation coefficient of 0.517, which confirmed the accuracy of the model. Nitrogen input as chemical fertilizer input and nitrogen output as crop harvest were respectively the main input and output factors in the study area. The most serious pollution was in 2006. The study provided important data needed to solve non-point agricultural pollution with important practical implications for the prevention and control of nitrogen pollution in groundwater.

     

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